Fuzzy reliability estimation for Frechet distribution by using simulation

The study has examined the estimation of Frechet distribution parameters with the shape parameter (α) and the scale parameter (β). Two estimation methods are used based on the maximum likelihood and Bayes methods. The life time data are fuzzy numbers. These estimations of parameters are employed to estimate the fuzzy reliability function of the distribution and to select the best estimate of the fuzzy reliability function by comparing the mean squares error (MSE) and the average absolute proportional error (MAPE). The results of simulation showed that the fuzziness is better than the real for all sample sizes and the fuzzy reliability at the estimates of the Bayes estimated is better than the maximum likelihood method. It gives the lowest average MSE and MAPE until to arrive at a minimum at sample size of n = 500

On a new double weighted exponential-pareto distribution: Properties and estimation

The main aim of this manuscript to present a new weighted distribution named the Double Weighted Exponential Pareto Distribution (DWEOD). This paper constructed and studied this new distribution. The quantifiable properties are discussed, including the mean, variance, harmonic mean, coefficient of variation, reliability function, moments generating function, mode, hazard function, and the reverse hazard function. Moreover, this work estimated the parameters of this distribution by the maximum likelihood estimation method and the method of the moment

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

The forecasting using the multi-layered neural network model is one of the methods used recently in forecasting, especially in climate forecasts for certain regions, because of its accuracy in forecasting, which sometimes reaches levels close to the real collected data. In this research, the daily temperatures in the climate of Iraq were predicted, by taking data from the Iraqi Meteorological Authority by (228) observations, which represent the daily temperatures of Karbala Governorate in the year (2021), The results of the autocorrelation and partial autocorrelation showed that the daily temperature series of Karbala governorate is unstable, and this was confirmed by conducting the augmented Dickey Fuller test. The data was analyzed using the multi-layered neural network model in two stages, and it was later shown that the accuracy of estimation and prediction using the multi-layered neural network even if the time series is not stable, The results showed an indication of an rising increase in temperatures during the coming years. The researcher concluded that it is necessary to pay attention to the vegetation cover and to conduct many predictive studies of the climate using the multi-layered neural network